U.S. patent application number 12/067185 was filed with the patent office on 2009-09-17 for iterative reconstruction with enhanced noise control filtering.
This patent application is currently assigned to Koninklijke Philips Electronics N.V.. Invention is credited to Mary K. Durbin, Lingxiong Shao, Jinghan Ye, Zuo Zhao.
Application Number | 20090232375 12/067185 |
Document ID | / |
Family ID | 37770951 |
Filed Date | 2009-09-17 |
United States Patent
Application |
20090232375 |
Kind Code |
A1 |
Ye; Jinghan ; et
al. |
September 17, 2009 |
ITERATIVE RECONSTRUCTION WITH ENHANCED NOISE CONTROL FILTERING
Abstract
An imaging system (10) comprises at least one radiation detector
(20) disposed adjacent a subject receiving aperture (18) to detect
radiation from a subject, receive the radiation and generate
measured data. An image processor (38) iteratively reconstructs the
detected radiation into image representations, in each
reconstruction iteration the image processor (38) applies noise
reduction algorithms to at least a difference between the measured
data and a portion of a previous iteration image
representation.
Inventors: |
Ye; Jinghan; (Fremont,
CA) ; Shao; Lingxiong; (Saratoga, CA) ; Zhao;
Zuo; (Palo Alto, CA) ; Durbin; Mary K.; (San
Jose, CA) |
Correspondence
Address: |
PHILIPS INTELLECTUAL PROPERTY & STANDARDS
P. O. Box 3001
BRIARCLIFF MANOR
NY
10510
US
|
Assignee: |
Koninklijke Philips Electronics
N.V.
Eindhoven
NL
|
Family ID: |
37770951 |
Appl. No.: |
12/067185 |
Filed: |
August 21, 2006 |
PCT Filed: |
August 21, 2006 |
PCT NO: |
PCT/IB06/52881 |
371 Date: |
March 18, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60720431 |
Sep 26, 2005 |
|
|
|
Current U.S.
Class: |
382/131 |
Current CPC
Class: |
A61B 6/037 20130101;
G06T 2207/10108 20130101; G06T 2207/10104 20130101; G06T 2211/424
20130101; G01T 1/2985 20130101; G06T 5/002 20130101; G06T
2207/10081 20130101; G06T 2207/30004 20130101; G06T 11/006
20130101 |
Class at
Publication: |
382/131 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. An imaging system comprising: at least one radiation detector to
detect radiation from a subject, receive the radiation and generate
measured data; and an image processor which iteratively
reconstructs the detected radiation into image representations, in
each reconstruction iteration the image processor applies noise
reduction algorithms to at least a variance between the measured
data and a portion of a previous iteration image
representation.
2. The system as set forth in claim 1, wherein the measured data
includes projection data and the image processor includes: a
forward projector which forward projects the previous iteration
image representation from an image memory, in which the iteration
image representation is iteratively reconstructed; a comparator
which compares the processed forward projected iteration image
representation with the processed measured projection data and,
based on the comparison, determines variance data; a back projector
which back projects the variance data into reconstructed variance
data; and a data updater, which updates the previous iteration
image representation with the reconstructed variance data into
reconstructed image data.
3. The system as set forth in claim 2, wherein the image processor
further includes: a variance data manipulator, which processes the
variance data before the back projecting with a noise reduction
algorithm.
4. The system as set forth in claim 3, wherein the image processor
further includes at least one of: a first data manipulator, which
processes the forward projected iteration image representation with
a noise reduction algorithm; and a second data manipulator, which
processes the measured projection data with a noise reduction
algorithm.
5. The system as set forth in claim 2, wherein the image processor
further includes: a fourth data manipulator, which processes the
reconstructed variance data with a noise reduction algorithm.
6. The system as set forth in claim 2, wherein the image processor
further includes a noise reduction mechanism which includes at
least two of: a first data manipulator, which processes the forward
projected iteration image representation with a first noise
reduction algorithm; a second data manipulator, which processes the
measured projection data with a second noise reduction algorithm; a
third data manipulator, which processes the variance data before
the back projecting with a third noise reduction algorithm; a
fourth data manipulator, which processes the reconstructed variance
data with a fourth noise reduction algorithm; and a fifth data
manipulator which processes the reconstructed image data with a
fifth noise reduction algorithm after updating.
7. The system as set forth in claim 6, wherein the at least two of
the first, second, third, fourth and fifth noise reduction
algorithms are the same type algorithms.
8. The system as set forth in claim 1, the detector is part of at
least one of: a PET scanner; a SPECT scanner; and a CT scanner.
9. A method of imaging comprising: detecting radiation from a
subject; generating measured data; and iteratively reconstructing
the detected radiation into image representations, in each
reconstruction iteration applying noise reduction algorithms to at
least a variance between the measured data and a portion of a
previous iteration image representation.
10. The method as set forth in claim 9, wherein the measured data
includes projection data and the step of reconstructing includes:
forward projecting a previous iteration image representation from
an image memory in which the iteration image representation is
iteratively reconstructed; comparing the processed forward
projected iteration image representation with the processed
measured projection data; based on the comparison, determining
variance data; back projecting the variance data into reconstructed
variance data; and updating the previous iteration image
representation with the reconstructed variance data.
11. The method as set forth in claim 10, wherein the step of
reconstructing further includes: processing the variance data with
a noise reduction algorithm before back projecting.
12. The method as set forth in claim 1, wherein the step of
reconstructing further includes at least one of: processing the
forward projected iteration image representation with a noise
reduction algorithm; and processing the measured projection data
with a noise reduction algorithm.
13. The method as set forth in claim 10, wherein the step of
reconstructing further includes: processing the reconstructed
variance data with a noise reduction algorithm.
14. The method as set forth in claim 9, wherein the step of
reconstructing includes: comparing each iteration image
representation with an end criteria; terminating the iterative
reconstruction in response to the end criteria being met; and
processing a final iterative image representation with a noise
reduction algorithm.
15. The method as set forth in claim 14, wherein the final image
representation is not filtered.
16. The method as set forth in claim 9, wherein the step of
iterative reconstructing includes: forward projecting a current
iteration image representation; applying corresponding noise
reduction operations to the forward projected iteration image
representation and the measured data; determining a variation
between the noise reduced forward projected image representation
and the measured data; and modifying the current iteration image
representation in accordance with the variation.
17. The method as set forth in claim 16, wherein the iterative
reconstructing applied is: .lamda. n + 1 = .lamda. n j w ij F 2 { p
j } F 1 { l w lj .lamda. n } j w ij ##EQU00005## where F.sub.1{ }
indicates filtering or processing or other noise reducing
manipulating of the forward projected data; and F.sub.2{ }
indicates filtering or processing or other noise reducing
manipulating of the measured data.
18. An image processor, which iteratively reconstructs input image
data into image representations, the image processor comprising: a
forward projector, which forward projects previous iteration image
representation from an image memory, in which the iteration image
representation is iteratively reconstructed; a first data
manipulator, which manipulates the forward projected iteration
image representation with a first noise reduction algorithm; a
second data manipulator, which manipulates the input image data
with a second noise reduction algorithm; a comparator, which
compares the manipulated forward projected iteration image
representation with the manipulated input image data and, based on
the comparison, determines variance data; a back projector, which
back projects the variance data into reconstructed variance data;
and a data updater, which updates the previous iteration image
representation with the reconstructed variance data into
reconstructed image data.
19. The system as set forth in claim 18, wherein the first and
second noise reduction algorithms are the same type of the
algorithms.
20. The system as set forth in claim 18, wherein the image
processor further includes: a third data manipulator, which
manipulates the variance data before the back projecting with a
noise reduction algorithm.
21. The system as set forth in claim 20, wherein the image
processor further includes: a fourth data manipulator, which
manipulates the reconstructed variance data after the back
projecting with a noise reduction algorithm.
22. A method of forming a medical image comprising: inputting
measured medical image data; iteratively reconstructing the medical
image using the measured medical image data and estimated medical
image data; wherein the iterative reconstruction includes filtering
noise from at least two of the measured medical image data, the
estimated medical image data, and a variance between the measured
medical image data and the estimated medical image data.
23. The method of claim 22 wherein the iterative reconstruction
further comprises filtering noise from the measured medical image
data, the estimated medical image data, and a variance between the
measured medical image data and the estimated medical image
data.
24. The method of claim 23 wherein the iterative reconstruction
further comprises filtering noise prior to forming a final
iterative image.
25. The method of claim 22 wherein the filtering noise step is
performed using the same type of algorithms.
26. A method of forming a medical image comprising: inputting
measured medical image data; iteratively reconstructing the medical
image using the measure medical image data and estimated medical
image data, wherein a dual filtering technique is used in order to
provide more consistent image quality in the medical image over a
wider range of count statistics.
27. The method of claim 26, wherein the dual filtering technique
used includes filtering noise from at least two of the measured
medical image data, the estimated medical image data, and a
variance between the measured medical image data and the estimated
medical image data.
28. The method of claim 26 wherein the dual filtering technique
used includes filtering different noise data with the same type of
algorithm.
Description
[0001] The present invention relates to the diagnostic imaging
systems and methods. It finds particular application in conjunction
with the Positron Emission Tomography (PET) and Single Photon
Emission Tomography (SPECT) systems and will be described with
particular reference thereto. It will be appreciated that the
invention is also applicable to other medical imaging systems such
as Computed Tomography systems (CT), and the like, and non-medical
imaging systems.
[0002] Nuclear medicine imaging employs a source of radioactivity
to image a patient. Typically, a radiopharmaceutical is injected
into the patient. Radiopharmaceutical compounds contain a
radioisotope that undergoes gamma-ray decay at a predictable rate
and characteristic energy. One or more radiation detectors are
placed adjacent to the patient to monitor and record emitted
radiation. Sometimes, the detector is rotated or indexed around the
patient to monitor the emitted radiation from a plurality of
directions. Based on information such as detected position and
energy, the radiopharmaceutical distribution in the body is
determined and an image of the distribution is reconstructed to
study the circulatory system, radiopharmaceutical uptake in
selected organs or tissue, and the like.
[0003] Typically, in the iterative reconstruction technique, an
estimate of the reconstructed volume of image data is forward
projected onto the plane of the detector. The forward projected
data is compared to the measured projection data. If the estimate
of the reconstructed image were perfect, these two projections of
data would match and there would be no difference. However, as the
image is being built, there typically is a difference or error. The
error or its inverse is then backprojected into the image volume to
correct the volumetric image and create a new estimate for the next
iteration.
[0004] Typically, the iterative reconstruction process continues
until the measured and forward projected data sets match within an
acceptable error. However, particularly in nuclear medicine, there
are noise issues. That is, the measured projection is contaminated
with noise and the forward projection is also contaminated with
noise. As a practical matter, the noise will never match. As a
result, the iterative process, if run for too long, can start to
degenerate the reconstructed image. One technique is to filter the
measured data or at a point during a reconstruction or filter the
reconstruction images. While such filtering helps to reduce noise
in an image, it also reduces image resolution.
[0005] The present invention provides a new and improved imaging
apparatus and method which overcomes the above-referenced problems
and others.
[0006] In accordance with one aspect, an imaging system is
disclosed. At least one radiation detector is disposed adjacent a
subject receiving aperture to detect radiation from a subject or
passing through a subject, receive the radiation and generate
measured data at a plurality of angles or a single angle. An image
processor iteratively reconstructs the detected radiation into
image representations, in each reconstruction iteration the image
processor applies noise reduction algorithms to at least a
difference between the measured data and a portion of a previous
iteration image representation.
[0007] In accordance with another aspect, a method of imaging is
disclosed. Radiation from a subject is detected. Measured data is
generated. The detected radiation is iteratively reconstructed into
image representations. In each reconstruction iteration noise
reduction algorithms are applied to at least a difference between
the measured data and a portion of a previous iteration image
representation.
[0008] In accordance with another aspect, an imaging processor,
which iteratively reconstructs input image data into image
representations, is disclosed. A forward projector projects
previous iteration image representation from an image memory, in
which the iteration image representation is iteratively
reconstructed. A first data manipulator manipulates the forward
projected iteration image representation with a first noise
reduction algorithm. A second data manipulator manipulates the
input image data with a second noise reduction algorithm. A
comparator compares the manipulated forward projected iteration
image representation with the manipulated input image data and,
based on the comparison, determines variance data. A third data
manipulator manipulates the variance data with a third noise
reduction algorithm. A back projector back projects the manipulated
variance data into reconstructed variance data. A data updater
updates the previous iteration image representation with the
reconstructed variance data into reconstructed image data.
[0009] One advantage resides in reducing the image noise while
minimizing the noise reduction impact on the original data.
[0010] Another advantage resides in better image resolution.
[0011] Still further advantages and benefits of the present
invention will become apparent to those of ordinary skill in the
art upon reading and understanding the following detailed
description of the preferred embodiments.
[0012] The invention may take form in various components and
arrangements of components, and in various steps and arrangements
of steps. The drawings are only for purposes of illustrating the
preferred embodiments and are not to be construed as limiting the
invention.
[0013] FIG. 1 is a diagrammatic illustration of an imaging
system;
[0014] FIG. 2 is a diagrammatic illustration of a portion of the
imaging system in detail;
[0015] FIG. 3 is a diagrammatic illustration of another portion of
the imaging system in detail; and
[0016] FIG. 4 is a diagrammatic illustration of yet another portion
of the imaging system in detail.
[0017] With reference to FIG. 1, a nuclear imaging system 10
typically includes a stationary gantry 12 that supports a rotatable
gantry 14. One or more detection heads 16 are carried by the
rotatable gantry 14 to detect radiation events emanating from a
region of interest or examination region 18. Alternately,
particularly in a PET scanner, the examination region is surrounded
by a ring of stationary detectors. Each detection head includes
two-dimensional arrays of detector elements or detector 20 such as
a scintillator and light sensitive elements, e.g. photomultiplier
tubes, photodiodes, and the like. Direct radiation signal to
electrical converters, such as CZT elements, are also contemplated.
Each head 16 includes circuitry 22 for converting each radiation
response into a digital signal indicative of its location (x, y) on
the detector face and its energy (z). The location of an event on
the detector 20 is resolved and/or determined in a two dimensional
(2D) Cartesian coordinate system with nominally termed x and y
coordinates. However, other coordinate systems are contemplated. In
one embodiment, a scatter grid and/or collimator 24 controls the
direction and angular spread, from which each element of the
detector 20 can receive radiation. Particularly in a SPECT scanner,
the detector 20 limits the reception of radiation only along known
rays. Thus, the determined location on the detector 20 at which
radiation is detected and the angular position of the camera 16
define the nominal ray along which each radiation event
occurred.
[0018] Typically, an object to be imaged is injected with one or
more radiopharmaceuticals or radioisotopes and placed in the
examination region 18 supported by a couch 26. Few examples of such
isotopes are Tc-99m, Ga-67, and In-111. The presence of the
radiopharmaceuticals within the object produces emission radiation
from the object. Radiation is detected by the detection heads 16
which are able to be angularly indexed or rotated around the
examination region 18 to collect the projection emission data at
one or more selected projection directions. The projection emission
data, e.g. the location (x, y), energy (z), and an angular position
(.theta.) of each detection head 16 around the examination region
18 (e.g., obtained from an angular position resolver 28) are stored
in a measured data memory 30.
[0019] With continuing reference to FIG. 1, an image processor 38
iteratively reconstructs a 3D image using noise reduction
algorithms at different stages of the reconstruction via a noise
reduction system or mechanism or means 40 as discussed in detail
below. In one embodiment, the image processor 38 executes a Maximum
Likelihood Expectation Maximization algorithm (MLEM). In
preparation for the first iteration of the reconstruction process,
an image memory 42 is initialized by loading the memory 42 with
assumed or first estimate of the image. The image estimates are
often characterized by uniform values inside the contour and zero
values-outside the contour. Alternately, the availability of
additional a priori information allows for more accurate first
estimate.
[0020] With continuing reference to FIG. 1 and further reference to
FIG. 2, the image processor 38 iteratively reconstructs 3D image
representation and stores a current image iteration in the image
memory 42. Each reconstruction iteration includes a forward
projection or transformation operation and a back projection or
transformation operation. A forward projector or estimator 50
creates current estimated projection data 52 from the current image
iteration stored in the image memory 42. A first or estimated data
manipulator 54 of the noise reduction mechanism 40 modifies or
processes the estimated data 52 to reduce or eliminate noise in the
estimated data 52. A modified estimated data is stored in a
modified estimated data memory 56. A second or measured data
manipulator 60 of the noise reduction mechanism 40 manipulates or
processes the measured projection data from the measured data
memory 30 to eliminate or reduce noise in the measured projection
data. The modified measured projection data is stored in a modified
measured data memory 62. A comparator 64 compares the modified
measured projection data with the modified estimated data along the
same direction to determine difference or variance data 66.
Optionally, a third or difference data manipulator 70 of the noise
reduction mechanism 40 modifies or processes the difference data 66
to reduce or eliminate noise in the difference data 66. A modified
difference data is stored in a modified difference data memory 72.
A back projector 74 back projects the modified difference data 72
to form a reconstructed difference image in a back projected or
reconstructed difference image memory 76. Optionally, a fourth data
manipulator 78 of the noise reduction mechanism 40 modifies or
processes the reconstructed difference image in the reconstructed
difference image memory 76 to reduce or eliminate noise in the
reconstructed difference image. An image updater 80 updates the
current image iteration in the image memory 42 with the
reconstructed difference image of the reconstructed difference
image memory 76. Optionally, a fifth or updated data manipulator 82
of the noise reduction mechanism 40 modifies or processes the
reconstructed image data in the image memory 42 to reduce or
eliminate noise in the reconstructed image data.
[0021] An end determining criteria processor 84 determines when to
stop the iterative reconstruction process. If the differences fall
below a preselected level, the iterative reconstruction process
ends. Optionally, a sixth or final data manipulator 86 of the noise
reduction mechanism 40 modifies or processes final reconstructed
image data 88 to reduce or eliminate noise in the final
reconstructed image data 88. The modified final reconstructed data
is stored in a modified final reconstructed image data memory 90
which may be the same memory as image memory 42. Optionally, images
retrieved from the final image memory may be filtered or
manipulated, e.g. smoothed, edge enhanced, or the like, as is
appropriate to the study and the preferences of the diagnostician.
In this manner, each successive iteration is performed with the
most recently updated image.
[0022] The examples of first, second, third, fourth, fifth and
sixth data manipulators are any type of processors or algorithms
capable of data manipulations to improve signal to noise ratio such
as high pass filter, low pass filter, Gaussian, Median filter and
Hanning filter. It is contemplated that all or some of the first,
second, third, fourth, fifth and sixth data manipulators are the
same type or different type data manipulators or filters, depending
on the system characteristics. More specifically to a preferred
embodiment, the first and second data manipulators apply matching
or corresponding algorithms. The remaining algorithms may be
different or eliminated.
[0023] A video processor 100 retrieves slices, projections, 3D
renderings, and other image information from the modified final
reconstructed image memory 90 and appropriately formats an image
representation for display on one or more human viewable displays,
such as a video monitor 102, printer, storage media, or the like.
If the video processor repeatedly retrieves the selected image
formation during reconstruction, the display will become clearer
with each iteration as the reconstructed image converges on a final
image.
[0024] With continuing reference to FIG. 2 and further reference to
FIG. 3, in this embodiment, the optional third, fourth, fifth and
sixth data manipulators 70, 78, 82, 86 are omitted from the noise
reduction mechanism 40. Only the estimated projection data and the
measured projection data are processed or manipulated via
corresponding first and second data manipulators 54, 60. Generally,
the MLEM Iterative Algorithm can be expressed as:
.lamda. n + 1 = .lamda. n j w ij p j l w lj .lamda. n j w ij ( 1 )
##EQU00001##
where .lamda..sup.n is the current estimate of the image, p.sub.j
is the measured projection data, and w.sub.ij is the probability
that a photon emitted from image space at position i is being
detected at position j at the detector.
[0025] The MLEM iterative algorithm for dual data manipulation, in
which the first and second data manipulators 54, 60 are used, can
be expressed as:
.lamda. n + 1 = .lamda. n j w ij F 2 { p j } F 1 { l w lj .lamda. n
} j w ij ( 2 ) ##EQU00002##
where F.sub.1{ } indicates filtering or processing or other noise
reducing manipulating of the estimated projection data; and
F.sub.2{ } indicates filtering or processing or other noise
reducing manipulating of the measured projection data.
[0026] In one embodiment, the same noise reduction filter is
applied to the measured projection data and the estimated
projection data. Applying a filter to the measured projection data
helps to control the noise in the measured projection data.
Applying the same filter in the estimated projection data tends to
cancel the blurring effect of the previous filter.
[0027] In this manner, by applying the dual filtering technique,
the random noise in the raw data and processing noise during
reconstruction are reduced while the impact of filtering on the
original signal is minimized.
[0028] With continuing reference to FIG. 2 and further reference to
FIG. 4, in this embodiment, the fourth, fifth and sixth data
manipulators 78, 82, 86 are omitted from the noise reduction
mechanism 40. The measured projection data, the estimated
projection data, and the difference data between the modified
measured projection data and the modified estimated projection data
are processed via corresponding first, second and third data
manipulators 54, 60, 70.
[0029] The MLEM iteration algorithm, in which the third data
manipulator 70 is used to process the difference data, can be
expressed as:
.lamda. n + 1 = .lamda. n j w ij F 3 { p j l w lj .lamda. n } j w
ij ( 3 ) ##EQU00003##
where F.sub.3{ } indicates processing or filtering or other noise
reducing manipulating of the difference data.
[0030] The MLEM Iterative Algorithm for triple data manipulation,
in which the first, second and third data manipulators 54, 60, 70
are used, can be expressed as:
.lamda. n + 1 = .lamda. n j w ij F 3 { F 2 { p j } F 1 { l w lj
.lamda. n } } j w ij ( 4 ) ##EQU00004##
where F.sub.1{ } indicates filtering or processing or manipulating
of the estimated projection data; F.sub.2{ } indicates filtering or
processing or manipulating of the measured projection data; and
F.sub.3 { } indicates filtering or processing or manipulating of
the difference data.
[0031] In one embodiment, the image processor 38 executes an
Ordered Subsets Expectation Maximization Algorithm (OSEM). The
measured projection data is divided into subsets. The second data
manipulator 60 modifies or processes one data subset at a time.
[0032] Of course it is also contemplated that the image processor
38 executes other alternative algorithms including Maximum A
Posteriori (MAP), Algebraic Reconstruction Technique (ART),
Iterative Filtered Back Projection (IFBP), and other like iterative
algorithms.
[0033] Although described with reference to 3D reconstruction, the
above methods and apparatuses are applicable to 2D and 1D image
restoration where any combination of same or different filters or
data manipulators described above is applicable to reduce or cancel
the noise while preserving the image data.
[0034] The invention has been described with reference to the
preferred embodiments. Obviously, modifications and alterations
will occur to others upon reading and understanding the preceding
detailed description. It is intended that the invention be
construed as including all such modifications and alterations
insofar as they come within the scope of the appended claims or the
equivalents thereof.
* * * * *